Linked Life Data

21377474

Source:http://linkedlifedata.com/resource/pubmed/id/21377474

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2011-4-15
pubmed:abstractText
The Michaelis-Menten equation has played a central role in our understanding of biochemical processes. It has long been understood how this equation approximates the dynamics of irreversible enzymatic reactions. However, a similar approximation in the case of networks, where the product of one reaction can act as an enzyme in another, has not been fully developed. Here we rigorously derive such an approximation in a class of coupled enzymatic networks where the individual interactions are of Michaelis-Menten type. We show that the sufficient conditions for the validity of the total quasi-steady state assumption (tQSSA), obtained in a single protein case by Borghans, de Boer and Segel can be extended to sufficient conditions for the validity of the tQSSA in a large class of enzymatic networks. Secondly, we derive reduced equations that approximate the network's dynamics and involve only protein concentrations. This significantly reduces the number of equations necessary to model such systems. We prove the validity of this approximation using geometric singular perturbation theory and results about matrix differentiation. The ideas used in deriving the approximating equations are quite general, and can be used to systematize other model reductions.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1095-8541
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 Elsevier Ltd. All rights reserved.
pubmed:issnType
Electronic
pubmed:day
7
pubmed:volume
278
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
87-106
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Reduced models of networks of coupled enzymatic reactions.
pubmed:affiliation
Department of Mathematics, University of Houston, Houston, TX 77204-3008, USA. ajitkmar@math.uh.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't